RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks
Users in social networks whose posts stay at the top of their followers’ feeds the longest time are more likely to be noticed. Can we design an online algorithm to help them decide when to post to stay at the top? In this paper, we address this question as a novel optimal control problem for jump stochastic differential equations. For a wide variety of feed dynamics, we show that the optimal broadcasting intensity for any user is surprisingly simple – it is given by the position of her most recent post on each of her follower’s feeds. As a consequence, we are able to develop a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post. Experiments on both synthetic and real data gathered from Twitter show that our algorithm is able to consistently make a user’s posts more visible over time, is robust to volume changes on her followers’ feeds, and significantly outperforms the state of the art.
RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks, by Ali Zarezade, Utkarsh Upadhyay, Hamid R. Rabiee, and Manuel Gomez Rodriguez, 10th International Conference on Web Search and Data Mining (WSDM), 2017. arXiv
More about this paper at RedQueen.
Correlated Cascades: Compete or Coopereate
In real world social networks, there are multiple cascades which are rarely independent. They usually compete or co- operate with each other. Motivated by the reinforcement the- ory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by us- ing the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior perfor- mance of the proposed model over the alternatives.
Correlated Cascades: Compete or Cooperate, by Ali Zarezade, Ali Khodadadi, Mehrdad Farajtabar, Hamid R. Rabiee, and Hongyuan Zha, Thirty-First AAAI Conference on Artificial Intelligence, 2017. arXiv.
The source codes, written in MATLAB, and datasets are available in the git repository. Please cite the above work if you use this software, and contact us in case of any problems.
Spatio-Temporal Modeling of Check-ins in Location-Based Social Networks
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users’ movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters using an efficient EM algorithm, which distributes over the users. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method better models the real data than other alternatives.
Spatio-Temporal Modeling of Check-ins in Location-Based Social Networks, by Ali Zarezade, Sina Jafarzadeh, and Hamid R Rabiee, arXiv preprint arXiv:1611.07710, 2016. arXiv.
The source codes, wrriten in MATLAB, and datasets are available in the git repository. Please cite the above work if you use this software and contact us in case of any problems.